1.Frequency and Risk Indicators of Periodontal Diseases in a Sample of Adult Egyptian Patients: A Hospital-Based Cross-Sectional Study
Omar Khaled Gami ; Dina FahimAhmed ; Khaled Mohamed Keraa ; Noha Ayman Ghallab ; Weam Elbattawy
Archives of Orofacial Sciences 2021;16(2):223-239
ABSTRACT
This hospital-based cross-sectional study aimed at determining frequency and risk indicators/predictors
of periodontitis in a sample of Egyptian adult population and to develop a prediction equation for
classifying periodontal diseases. Seven hundred and fifty subjects were consecutively recruited from
outpatient Diagnostic Center, Faculty of Dentistry, Cairo University. Validated oral health questionnaire
for adults and oral health impact profile-14 (OHIP-14) questionnaire were filled by all patients.
Diagnosis was made based on measurements of clinical periodontal parameters including plaque index,
bleeding on probing, pocket depth, clinical attachment level and gingival recession. Radiographic
examination was performed using digital periapical radiographs. Ordinal logistic regression analysis was
used to determine significant predictors of periodontal diseases and discriminant analysis was performed
to predict periodontal disease classification. Gingivitis was the most frequent periodontal disease (39.6%)
followed by periodontitis stage I (38%), stage II (20.4%), stage III (1.6%) and stage IV (0.4%). The
lowest OHIP-14 scores were in patients with periodontitis stages III and IV. Multivariate analysis showed
that education (p < 0.001), OHIP-14 score (p = 0.003), non-smoking (p = 0.001) and non-alcohol
drinking (p = 0.021) were significant negative predictors, while never to clean the teeth (p <0.001) and
cleaning the teeth once a month (p < 0.001) were significant positive predictors of periodontal disease.
Periodontitis stages III and IV were the least frequent on a sample of Egyptian adult patients. Education,
frequency of teeth cleaning, smoking, alcohol drinking and OHIP-14 scores were significant predictors
of periodontal disease. Through discriminant analysis this study could classify patients into different
periodontal diseases with an overall correct prediction of 99.2%.
Periodontal Diseases--epidemiology